R: Train a sits classifiction model using fast random forest...

sits_ranger {sits} | R Documentation

## Train a sits classifiction model using fast random forest algorithm

### Description

Use Fast Random Forest algorithm to classify data. This function is a front-end to the "ranger" method in the "ranger" package. Please refer to the documentation in that package for more details.

### Usage

    sits_ranger(data = NULL, num_trees = 2000, importance = "impurity", ...)

### Arguments

`data` | Time series with the training samples.
`num_trees` | Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times. (default: 2000).
`importance` | Variable importance mode, one of 'none', 'impurity', 'impurity_corrected', 'permutation'. The 'impurity' measure is the Gini index.
`...` | Other `ranger` parameters

### Value

Model fitted to input data (to be passed to `sits_classify`)

### Author(s)

Gilberto Camara, gilberto.camara@inpe.br

Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br

Rolf Simoes, rolf.simoes@inpe.br

### Examples

    # Retrieve the set of samples for Mato Grosso  (provided by EMBRAPA)
    samples_ndvi <- sits_select(samples_mt_6bands, bands = c("NDVI"))

    # Build a machine learning model
    ml_model <- sits_train(samples_ndvi, sits_ranger(num_trees = 100))

    # get a point and classify the point with the ml_model
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
    class <- sits_classify(point_ndvi, ml_model)

[Package sits version 0.13.0 Index]